---
title: Google Firestore (Native Mode)
---

> [Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's LangChain integrations.

This notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to [save, load and delete langchain documents](/docs/how_to#document-loaders) with `FirestoreLoader` and `FirestoreSaver`.

Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-firestore-python/).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-firestore-python/blob/main/docs/document_loader.ipynb)

## Before You Begin

To run this notebook, you will need to do the following:

* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)
* [Enable the Firestore API](https://console.cloud.google.com/flows/enableapi?apiid=firestore.googleapis.com)
* [Create a Firestore database](https://cloud.google.com/firestore/docs/manage-databases)

After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.

```python
# @markdown Please specify a source for demo purpose.
SOURCE = "test"  # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
```

### 🦜🔗 Library Installation

The integration lives in its own `langchain-google-firestore` package, so we need to install it.

```python
%pip install -qU langchain-google-firestore
```

**Colab only**: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.

```python
# # Automatically restart kernel after installs so that your environment can access the new packages
# import IPython

# app = IPython.Application.instance()
# app.kernel.do_shutdown(True)
```

### ☁ Set Your Google Cloud Project

Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.

If you don't know your project ID, try the following:

* Run `gcloud config list`.
* Run `gcloud projects list`.
* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113).

```python
# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.

PROJECT_ID = "my-project-id"  # @param {type:"string"}

# Set the project id
!gcloud config set project {PROJECT_ID}
```

### 🔐 Authentication

Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.

* If you are using Colab to run this notebook, use the cell below and continue.
* If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env).

```python
from google.colab import auth

auth.authenticate_user()
```

## Basic Usage

### Save documents

`FirestoreSaver` can store Documents into Firestore. By default it will try to extract the Document reference from the metadata

Save langchain documents with `FirestoreSaver.upsert_documents(<documents>)`.

```python
from langchain_core.documents import Document
from langchain_google_firestore import FirestoreSaver

saver = FirestoreSaver()

data = [Document(page_content="Hello, World!")]

saver.upsert_documents(data)
```

#### Save documents without reference

If a collection is specified the documents will be stored with an auto generated id.

```python
saver = FirestoreSaver("Collection")

saver.upsert_documents(data)
```

#### Save documents with other references

```python
doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = FirestoreSaver()

saver.upsert_documents(documents=data, document_ids=doc_ids)
```

### Load from Collection or SubCollection

Load langchain documents with `FirestoreLoader.load()` or `Firestore.lazy_load()`. `lazy_load` returns a generator that only queries database during the iteration. To initialize `FirestoreLoader` class you need to provide:

1. `source` - An instance of a Query, CollectionGroup, DocumentReference or the single `\`-delimited path to a Firestore collection.

```python
from langchain_google_firestore import FirestoreLoader

loader_collection = FirestoreLoader("Collection")
loader_subcollection = FirestoreLoader("Collection/doc/SubCollection")


data_collection = loader_collection.load()
data_subcollection = loader_subcollection.load()
```

### Load a single Document

```python
from google.cloud import firestore

client = firestore.Client()
doc_ref = client.collection("foo").document("bar")

loader_document = FirestoreLoader(doc_ref)

data = loader_document.load()
```

### Load from CollectionGroup or Query

```python
from google.cloud.firestore import CollectionGroup, FieldFilter, Query

col_ref = client.collection("col_group")
collection_group = CollectionGroup(col_ref)

loader_group = FirestoreLoader(collection_group)

col_ref = client.collection("collection")
query = col_ref.where(filter=FieldFilter("region", "==", "west_coast"))

loader_query = FirestoreLoader(query)
```

### Delete documents

Delete a list of langchain documents from Firestore collection with `FirestoreSaver.delete_documents(<documents>)`.

If document ids is provided, the Documents will be ignored.

```python
saver = FirestoreSaver()

saver.delete_documents(data)

# The Documents will be ignored and only the document ids will be used.
saver.delete_documents(data, doc_ids)
```

## Advanced Usage

### Load documents with customize document page content & metadata

The arguments of `page_content_fields` and `metadata_fields` will specify the Firestore Document fields to be written into LangChain Document `page_content` and `metadata`.

```python
loader = FirestoreLoader(
    source="foo/bar/subcol",
    page_content_fields=["data_field"],
    metadata_fields=["metadata_field"],
)

data = loader.load()
```

#### Customize Page Content Format

When the `page_content` contains only one field the information will be the field value only. Otherwise the `page_content` will be in JSON format.

### Customize Connection & Authentication

```python
from google.auth import compute_engine
from google.cloud.firestore import Client

client = Client(database="non-default-db", creds=compute_engine.Credentials())
loader = FirestoreLoader(
    source="foo",
    client=client,
)
```
